Alaa Mahmood, İsa Avcı^*

Computer Systems Science and Engineering, Vol.49, pp. 401-417, 2025, DOI:10.32604/csse.2025.062413 - 25 April 2025

Abstract A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies.… More >

Pengcheng Gao^*, Zhenjiang Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025

Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >

Yue Yang^1,2, Xiangyan Tang^2,3,*, Zhaowu Liu^2,3,*, Jieren Cheng^2,3, Haozhe Fang³, Cunyi Zhang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4389-4408, 2025, DOI:10.32604/cmc.2025.060357 - 06 March 2025

Abstract With the rapid development of Internet of Things technology, the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast, bringing unprecedented challenges to the field of network security, especially in identifying malicious attacks. However, due to the uneven distribution of network traffic data, particularly the imbalance between attack traffic and normal traffic, as well as the imbalance between minority class attacks and majority class attacks, traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data. To effectively tackle this challenge, we have designed a lightweight… More >

Yicong Yu^1,2, Kaixin Lin¹, Jiajun Hong¹, Rong-Guei Tsai^3,*, Yuanzhi Huang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 913-928, 2025, DOI:10.32604/cmc.2024.058467 - 03 January 2025

Abstract In recent years, the number of patients with colon disease has increased significantly. Colon polyps are the precursor lesions of colon cancer. If not diagnosed in time, they can easily develop into colon cancer, posing a serious threat to patients’ lives and health. A colonoscopy is an important means of detecting colon polyps. However, in polyp imaging, due to the large differences and diverse types of polyps in size, shape, color, etc., traditional detection methods face the problem of high false positive rates, which creates problems for doctors during the diagnosis process. In order to… More >

Chunhui Li^1,2, Xiaoying Wang^1,2,*, Qingjie Zhang^1,2, Jiaye Liang¹, Aijing Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 645-674, 2025, DOI:10.32604/cmc.2024.058081 - 03 January 2025

Abstract Previous studies have shown that deep learning is very effective in detecting known attacks. However, when facing unknown attacks, models such as Deep Neural Networks (DNN) combined with Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with LSTM, and so on are built by simple stacking, which has the problems of feature loss, low efficiency, and low accuracy. Therefore, this paper proposes an autonomous detection model for Distributed Denial of Service attacks, Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention (MSCNN-BiGRU-SHA), which is based on a Multi-strategy Integrated Zebra Optimization Algorithm (MI-ZOA). The… More >

Gang Long, Zhaoxin Zhang^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 327-343, 2024, DOI:10.32604/cmc.2024.054558 - 15 October 2024

Abstract Network anomaly detection plays a vital role in safeguarding network security. However, the existing network anomaly detection task is typically based on the one-class zero-positive scenario. This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set. This phenomenon is known as prediction drift. Additionally, the rarity of anomaly data, often masked by normal data, further complicates network anomaly detection. To address these challenges, we propose the PUNet network, which ingeniously combines the strengths of traditional machine learning and deep learning techniques… More >

Muaadh A. Alsoufi^1,*, Maheyzah Md Siraj¹, Fuad A. Ghaleb², Muna Al-Razgan³, Mahfoudh Saeed Al-Asaly³, Taha Alfakih³, Faisal Saeed²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112 - 20 August 2024

Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

Yanhua Liu^1,2,3, Yuting Han^1,2,3, Hui Chen^1,2,3, Baokang Zhao^4,*, Xiaofeng Wang⁴, Ximeng Liu^1,2,3

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1851-1866, 2024, DOI:10.32604/cmc.2024.051194 - 15 August 2024

Abstract As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network’s generalization capabilities. Experimental results show that the proposed method can efficiently distinguish More >

Xiu Liu, Liang Gu^*, Xin Gong, Long An, Xurui Gao, Juying Wu

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442 - 20 June 2024

Abstract With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for… More >

Manal Abdullah Alohali¹, Muna Elsadig¹, Fahd N. Al-Wesabi², Mesfer Al Duhayyim³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴

Computer Systems Science and Engineering, Vol.48, No.3, pp. 759-771, 2024, DOI:10.32604/csse.2023.037305 - 20 May 2024

Abstract Owing to the rapid increase in the interchange of text information through internet networks, the reliability and security of digital content are becoming a major research problem. Tampering detection, Content authentication, and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies. The authors’ difficulties were tampering detection, authentication, and integrity verification of the digital contents. This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection (ADMDTW-TAD) via the Internet. The DM concept is exploited in the presented… More >